Research Groups Materials Modelling

Contact

Name

Angela Quadfasel

Group Manager Materials Modelling I

Phone

work
+49 241 80 95948

Email

E-Mail

Contact

Name

Jannik Gerlach

Group Manager Materials Modelling II

Phone

work
+49 241 80 95920

Email

E-Mail
 

The Materials Modelling groups deal with microstructure evolution during forming processes and in corresponding process chains. The groups utilize macroscopic as well as multi-scale modelling techniques to predict grain size, texture and precipitation.

The group Materials Modelling I in addition is concerned with modelling phenomena occurring at different types of interfaces. Notably the interaction between tool and work piece, e.g. during skin-pass rolling, as well as the bond formation during roll bonding of metals. In addition, unavoidable phenomena like wear in metal forming are a major point of concern.

The group Materials Modelling II in addition is concerned with digitization in metal forming. The group uses intelligent methods based on machine learning to efficiently aggregate and evaluate data. For predictive modelling, the group combines physical, inverse and databased approaches to match requirements.

 
 

Investigation of Skin-Pass Rolling With a Focus on Surface

Sketch of the skin-pass process with mill finish and EDT surface Copyright: © IBF Sketch of the skin-pass process with mill finish and EDT surface

An important characteristic of rolled aluminium strips for use in the automotive outer skin is the surface quality. The topography of the surface and in particular the number of roughness peaks as well as the volume of closed lubrication pockets influence the success of the subsequent process steps deep drawing and painting.
The work carried out so far has investigated the relationship between the process kinematics of skin-pass rolling and the transfer mechanisms. For this purpose, the kinematics of a process model of flat rolling was transferred to a mesomodel to describe the surface imprinting. With regard to the imprinting of the surface, a good correspondence between simulation and experiment could be shown.
In the medium term, the numerical model is intended to enable a knowledge-based design of the skin-pass process for aluminium alloys, taking into account global and local influences.

For further information, please contact Angela Quadfasel.

 
Simulation of Surface Indentation at Aluminium Skin-Pass Rolling
Simulation of surface indentation at aluminium skin-pass rolling
 
 

Pass Schedule Design via Machine Learning

Interaction of influencing parameters during pass schedule design for rolling Copyright: © IBF Interaction of influencing parameters during pass schedule design for rolling

The design of pass schedules for rolling is based on expert and empirical knowledge. This is due to each pass influencing all subsequent passes. Machine learning algorithms could provide an approach to automatize the design of pass schedules. By training them they can derive knowledge from data without needing an explicit mathematical formulation. As a proof of concept a data base has been generated using a fast process model. A neural network has then been trained to design pass schedules based on the data provided, if the initial and final state of the workpiece are provided as input. The boundary conditions are given by the universal rolling mill available at the IBF. The designed pass schedule fulfills all boundary conditions while exactly meeting the final state of the workpiece. Therefor the automatized design of pass schedules using machine learning algorithms seems feasible.

For further information, please contact Christian Idzik.

 
 

Simulation of the Process Chain for a Turbine Disc

Turbine disc process chain and position in the engine Copyright: © Leistritz, SMS, IBF Turbine disc process chain and position in the engine

The production of turbine discs for aerospace applications is characterized by very strict safety requirements including tight windows for the microstructure. The evolution of the microstructure therefor needs to be accounted for during the design of the process chain. Accordingly an online-coupling between StrucSim, a program calculating the microstructure, and the commercial finite element, short FE, Software Simufact was developed. This means that StrucSim is called during the FE Simulation and influencing its results. Subsequently the process chain was reproduced in FE Simulations and calculated using the online-coupling. Thereby the microstructure evolution was calculated for the whole workpiece along the process chain. This technique can be used to optimize processes or process chains regarding productivity or reproducibility in the future.

For further information, please contact Jannik Gerlach.

 
 

Fast Process Models for Rolling

Single pass during rolling including force, temperature and microstructure evolution Copyright: © IBF Single pass during rolling including force, temperature and microstructure evolution

Fast process models enable the accurate simulation of heavy plate rolling on the industrial and laboratory scale. Based on the pass schedule and material parameters it predicts the most important properties, such as force, temperature and microstructure, within seconds. Thus it has a wide range of applications, particularly in the field of design and optimization. With Industry 4.0 in mind, it has been coupled to a data base of industrial trials resulting in the ability to determine material parameters from just the measured forces. It has furthermore been coupled with machine learning algorithms to automatically design pass schedules for the universal rolling mill at the IBF. Fast process models are also being used for teaching and seminars, supplemented by a specially created graphical user interface. It allows students and seminar participants to develop an intuitive approach to the design, calculation and optimization of pass schedule as well as a detailed understanding of the underlying mechanisms.

For further information, please contact Christian Idzik.

 
Fast Rolling Models for Pass Schedule Design
Fast rolling models for pass schedule design
 
 

Predicting the texture in fast models

Texture development during cold rolling Copyright: © IBF Texture development during cold rolling

A more resource-efficient E-mobility requires increased efficiency of electrical drives. Optimizing the texture to reduce iron losses is one possibility to achieve this goal. There are models that can predict the texture development. However, these models employ calculation times of several hours. Therefore, the goal of the ERS Seed Fund Projects is to develop a fast model to predict the texture within seconds. An FFT-Solver from DAMASK provides a basis for the development of a fast model. The solver uses the deformation gradient from a forming process and applies it to a representative volume element to calculate the texture. A method called Model Order Reduction can reduce the computational time by interpolating between snapshots. Snapshots are results calculated beforehand serving as reference points. The universal rolling mill at the IBF, Abaqus and other simulations tools serve as comparisons to validate the results.

For further information, please contact Aditya Vuppala.

 
 

Finite-Element Based Process Design for Fabrication of Metal Composites by Roll Bonding

FE model for simulating bond strength evolution during Roll Bonding Copyright: © IBF, Hydro FE model for simulating bond strength evolution during Roll Bonding

Roll Bonding enables the production of composites with customized combinations of properties. In roll bonding, the bonding partners are permanently joined together by plastic deformation. The bond formation is a complex process influenced by material properties and process parameters. At IBF an Abaqus subroutine has been developed for computing the formation and failure of the bonds. In a DFG transfer project, this subroutine will be further improved to develop efficient process routes for new material combinations. With this subroutine and the Abaqus process model, Roll Bonding can now be mapped. The bond strength is calculated depending on the surface enlargement. The established bond can also loosen again due to unfavorable load condition after roll gap. The influences of parameters such as temperature and height reduction on the bond strength and the bonding status can now be simulated.

For further information, please contact Zhao Liu.

  Simulation of roll bonding
 
 

High Manganese Steel Crashboxes

Experimental and simulated high manganese steel crashbox Copyright: © IBF Experimental and simulated high manganese steel crashbox

High manganese steels, short HMnS, have a high energy absorption potential due to their extraordinary combination of strength and formability. This qualifies HMnS as potential materials for crash relevant components in the automotive industry. However, the available elongations up to 70% are not reached in the crash of thin walled structures. In order to use HMnS for crash-relevant lightweight structures, various measures have to be taken. These include an adapted alloy design and the adjustment of a tailored microstructure with increased yield strength. Thus, a defined deformation behavior with maximum energy absorption should be achieved. Accompanying the experimental investigation of the optimal material properties, the crash behavior is predicted by multi- scale simulation. Therefore, a physical-based hardening model with input data from ab initio calculations is coupled with the FEM simulation.

For further information, please contact Angela Quadfasel.

 
 

Microstructure Simulation with DIGIMU® and StrucSim

Comparison of microstructure from DIGIMU® and from compression test Copyright: © IBF Comparison of microstructure from DIGIMU® and from compression test

DIGIMU®, developed by the software manufacturer TRANSVALOR S.A., and StrucSim, developed at the IBF, are programs for the simulation of the microstructural development during hot forming. DIGIMU® is based on physical approaches and allows a spatially resolved representation of grain size evolution and average dislocation density. The optimization of the material model parametrization, as well as the targeted application for industrial forming processes are ongoing work in close cooperation with TRANSVALOR. In StrucSim, the microstructure of the material is described by state variables that evolve depending on the process parameters. Thus, microstructural variables such as the mean grain size or recrystallized (RX) fraction can be calculated and the flow stress derived from them. Empirical modelling approaches allow here a low computational time for coupling with fast process models to calculate the evolution of grain size distributions and RX fractions.

For further information, please contact Holger Brüggemann.

 
Microstructure Calculation with StrucSim for Rolling and Forging
Microstructure calculation with StrucSim for rolling and forging
 
 

Thermo-Mechanical Design of Microstructures for Damage Control

Thermomechanical treatment for microstructural variation Copyright: © IBF Thermomechanical treatment for microstructural variation

As part of the Collaborative Research Center “TRR 188 – Schädigungskontrollierte Umformprozesse” this project aims to determine the influence of hot working microstructures on the damage initialization and evolution in subsequent cold working processes. Therefore, possible microstructural variations for two different steel types with regards to phase composition, morphology and grain size are calculated via the CALPHAD method. Afterwards a deformation dilatometer is used to process specimens reproducing these microstructures. Finally, these specimens are exposed to typical load paths of cold rolling and cold forging processes on a laboratory scale. The resulting damage is characterized and quantified and will later be used to develop optimized process strategies that allow damage control in microstructures during hot working.

For further information, please contact Jannik Gerlach.

 
 

Precipitation in Aluminium Alloys in AMAP Project P19

Process chain and consortium in the AMAP P19 project Copyright: © AMAP Process chain and consortium in the AMAP P19 project

Due to their high strength, good formability, corrosion resistance and relative low density, age hardable aluminium alloys of the system Al-Mg-Si (AA6xxx) exhibit a great potential for many light weight applications such as body-sheets for the automobile industry. For these alloys the high strength is mainly obtained by the precipitation microstructure. Within the industrial manufacturing, the characteristics and the evolution of the precipitation microstructure are determined by the time-temperature profile of the thermo-mechanic process chain. Usually the final strength is obtained by age hardening at elevated temperatures in the last process step. In order to optimize the manufacturing process regarding the in-service properties of the workpiece, a coupled tool to simulate the microstructure evolution at low/high temperatures as well as the resulting mechanical properties is developed at the IBF in the course of the AMAP project P19.

For further information, please contact Fabrice Wagner.

 
 

Cold Rolling Strategies for Producing Magnetic-Optimized Electrical Steel Sheet in Energy-Efficient Electrical Drives

Multi-scale model for simulating texture evolution during cold rolling Copyright: © IBF, IMM Multi-scale model for simulating texture evolution during cold rolling

One way to increase the efficiency of electric drives is to optimize the magnetic properties of the electrical steel used in the magnetic core. In order to quantify the influence of process parameters on these final properties and to create a scientific-theoretical basis for the development of low-loss electrical steel, an interdisciplinary DFG research group, FOR 1897, is working on the integrated process chain modeling. The main task of the IBF is to investigate and simulate the cold rolling process. Experimentally, the IBF will test different rolling strategies on the cold and hot rolling mill. A multi-scale model that includes a macroscopic finite element model and a microscopic crystal plasticity finite element model is created to compute the texture evolution, which makes it possible to determine the influence of different rolling strategies and initial states on the local texture development during cold rolling. By linking the sub-models, it enables model-based process design of low-loss electrical sheets for highly efficient electric drives.

For further information, please contact Johannes Lohmar.

 
 

FepiM-Algorithm, flow curve determination through explicit pointwise inverse modelling

Iterative adjustment of the flow curve in a single increment Copyright: © IBF Iterative adjustment of the flow curve in a single increment

Determining flow curves directly from experimental outputs sometimes is non-trivial, especially when the deformation is inhomogeneous due to friction for example. The state of art inverse methods for flow curve determination fit the experimental force displacement curves with finite element (FE) simulation, thereby considering the geometry changes during deformation. However, these methods are cost and time inefficient, as well as requiring a predefined mathematical equation describing the flow curve. The FepiM algorithm is an inverse FE based method and determines the flow curve as tabular data. The flow stress at each increments in the simulation is determined by matching the simulated and experimental force at the current displacement. Therefore, the flow curve can be determined as tabular data, eliminating the necessity of mathematical equations to describe the flow curve. In a corresponding research project, different strategies to predict the flow stress are investigated, such as heuristic or iterative approaches. The final target is to enable fast flow curve extraction from inhomogeneous conditions.

For further information, please contact Aditya Vuppala.

 
 

Closed-Die Forging of 6000 Series Aluminium

Example of temperature development during closed-die forging Copyright: © IBF Example of temperature development during closed-die forging

6000 series aluminum alloys are Al-Mg-Si alloys characterized by their good precipitation hardenability. However, the mechanical properties, such as strength, are determined not only by the precipitations but also by the grain size. Therefore, it is necessary to control this quantity also along a complex thermomechanical process chain. Within a ZIM cooperation project with the company Weisensee Warmpressteile GmbH, this issue is to be investigated on the basis of drop-forged components with subsequent heat treatment and aging. The aim is to develop a process design strategy that is able to specifically adjust the microstructure and, accordingly, the mechanical properties through targeted control of the forging and heat treatment sequence.

For further information, please contact Holger Brüggemann.

 
 

Phase Transformation in Nickel-Base Alloys

Schematic TTT diagram for Inconel 718 Copyright: © IBF Schematic TTT diagram for Inconel 718

Nickel-base alloys exhibit good corrosion and high-temperature properties. Due to their high creep resistance, they are ideally suited for usage under extreme conditions e.g. aircraft engines. Therefor the precipitation microstructure and the kinetics of phase transformations are the most decisive factors for the good creep resistance at high temperatures. Within the framework of a ZIM project, a software tool is currently being developed at the IBF in cooperation with the company GTT* for the calculation of phase transformation kinetics and TTT diagrams of nickel-base alloys. This tool will be used to predict phase fractions as a function of alloy composition and temperature profile, thus enabling a simulation-based optimization of the processing of these alloys. Further, this software-tool will be used for various other metallic alloys and slags in the future.

*Gesellschaft für Technische Thermochemie und -physik mbH

For further information, please contact Fabrice Wagner.

 
 

Finite-Element Based Estimation of the Lifespan of Wear-Resistant Coating for Cold Rolling

Multiscale FE model for simulating coating behaviour during cold rolling Copyright: © IBF Multiscale FE model for simulating coating behaviour during cold rolling

For cold rolling of steel, due to large plastic deformation, the periodic mechanical stress on work rolls is quite significant and often causes severe wear, which terminates the production and reduces the efficiency. To increase the lifespan of the work rolls, wear-resistant coatings are applied. However, the lifespan of work rolls with a specific coating category and thickness cannot be scientifically estimated, which causes difficulties in the coating selection and application for other forming technologies. In this project, multi-scale FE models are used to estimate the lifespan of coatings. In macro model the kinematics and stress in roll gap are simulated and afterwards transferred to a meso model as boundary conditions. In the meso model, the coating behaviour can be simulated using a bonding model under realistic forming condition as in the roll gap.

For further information, please contact Zhao Liu.

 
 

Data based process control along intercompany process chains

Demonstrator process chain and infrastructure Copyright: © IBF Demonstrator process chain and infrastructure

Typical process chains for forming products include several companies, which place requirements on their suppliers regarding the product specifications. Random Inspections ensure that the requirements of both incoming and outgoing goods are fulfilled. As a result, the exact material properties of most of the individual products in a batch are unknown and deviations may stay undetected. In order to reliably produce good parts despite above factors, product specifications and processes are defined more restrictively than necessary. In addition, it is not possible to react to unknown deviations, which may result in increased product rejections and reduced process efficiency. The aim of this project is to investigate how a standardizable, forgery-proof and protected transfer of model-based determined product properties between companies within a process chain can take place in order to predict the properties for each individual product and react to deviations through process adaptations.

For further information, please contact Nilesh Thakare.